Trading

Kerry Back

Workflow

Spreadsheets
- Positions (initially all zero)
- Ranks (from train and predict)
- Prices (most recent stock prices)
(Positions + Ranks + Prices) $\rightarrow$ Trades
(Positions + Trades) $\rightarrow$ updated Positions

Initial portfolio

Cash to invest
Select numstocks, equal weight (for example)
From Ranks, get top numstocks
(cash/numstocks) = $ per stock
From prices, calculate shares = $ per stock / price
$\Rightarrow$ Trades $\Rightarrow$ Positions

Trading

Update cash = cash + interest
New Ranks (no longer holding just top stocks)
New Prices (no longer equally weighted)
Need to decide how (Positions + Ranks + Prices) $\rightarrow$ Trades

Possible strategy

If new rank is below some threshold, sell all shares
Otherwise, if new weight is above some threshold, sell some shares
Invest updated cash + proceeds from sales as
- Start with best stock with no current holding
- Buy enough shares to hit target weight
- Then next best stock, etc., until out of cash

Python digression

Suppose ser is a pandas series. What is

2*ser

What is

2 > 1

What is

ser > 1

If x is another pandas series with the same index, what is

x[ser > 1]

What is

10 if 2 > 1 else 20

If ser is a pandas series, what is

np.where(ser>1, 10, 20)

Example

26 stocks in universe. Current ranks and prices:

ranks = pd.read_excel("files/ranks0.xlsx", index_col=0)
ranks = ranks.squeeze()

prices = pd.read_excel("files/prices0.xlsx", index_col=0)
prices = prices.squeeze()

$100,000 to invest, want to hold 5 stocks equally weighted

equity = cash = 100000
weight = 0.2

Initial portfolio

buys = ranks<=5
buyamounts = (weight*equity/prices).astype(int)

trades = np.where(buys, buyamounts, 0)
trades = pd.Series(trades, index=ranks.index)

positions = trades
cash -= (trades*prices).sum()

Here are the trades:

A      0
B      0
C      0
D    810
E      0
F      0
G      0
H    294
I    573
J    244
K      0
L      0
M      0
N      0
O    481
P      0
Q      0
R      0
S      0
T      0
U      0
V      0
W      0
X      0
Y      0
Z      0
dtype: int32

Update ranks, prices, and equity

Update ranks and prices (normally run model and check prices):

ranks = pd.read_excel("files/ranks1.xlsx", index_col=0)
ranks = ranks.squeeze()

prices = pd.read_excel("files/prices1.xlsx", index_col=0)
prices = prices.squeeze()

Update portfolio value (normally would update from broker):

equity = cash + (positions*prices).sum()
weights = (positions*prices) / equity

Sells

sellall = (ranks>10) & (positions !=0)
sellallamounts = positions
trades = - np.where(sellall, sellallamounts, 0)

sellsome = (ranks<=10) & (weights>0.2)
sellsomeamounts = (equity*(weights-0.2)/prices).astype(int)
trades -= np.where(sellsome, sellsomeamounts, 0)

trades = pd.Series(trades, index=ranks.index)
cash -= (trades*prices).sum()

Buys

Would like a list of the tickers not currently owned, starting with best stock.
Can get it like this:

tickers = ranks[positions==0].sort_values().index.to_list()

What does tick = tickers.pop(0) do?
- tick is the first ticker in the list
- tick is removed from the list (popped off)
Want to loop over tickers until cash is exhausted.

Use a “while loop” ($500 is arbitrary):

while cash >= 500:
    tick = tickers.pop(0)
    dollars = min(cash, weight*equity)
    trades[tick] = int(dollars/prices[tick])
    cash -= trades[tick]*prices[tick]

positions += trades

Here are the trades:

A      0
B    401
C      0
D    -40
E      0
F      0
G      0
H      0
I      0
J     -6
K      0
L      0
M      0
N     21
O   -481
P      0
Q      0
R      0
S      0
T      0
U      0
V      0
W      0
X      0
Y      0
Z      0
dtype: int32

Here are the ranks that generated the trades:

A    19
B     1
C    24
D     8
E    23
F    17
G    26
H     2
I     4
J    10
K    16
L    13
M    21
N     3
O    12
P     7
Q    11
R    15
S     6
T    14
U    18
V    25
W     9
X     5
Y    20
Z    22
Name: 0, dtype: int64